ArticleProceedings of SPIE--the International Society for Optical Engineering2014
Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluating attenuation correction strategies in a dedicated, single-gantry breast PET-tomosynthesis scanner.Physics in medicine and biology · 2020Article
- X-ray induced acoustic computed tomography.Photoacoustics · 2020Review
- Low-dose spectral CT reconstruction using image gradientApplied mathematical modelling · 2018Article
- Dosimetry in x-ray-based breast imaging.Physics in medicine and biology · 2016Article
- A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.IEEE transactions on bio-medical engineering · 2016Article
- Quantitative Diagnosis of Tongue Cancer from Histological Images in an Animal Model.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Spectral-Spatial Classification Using Tensor Modeling for Cancer Detection with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging.Journal of biomedical optics · 2014Article
- 3DProceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
4 authors.
Funding
Abstract
Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.
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